How SAS Delivers Better Merchandise Planning & Price Optimization
Our innovative omnichannel analytic capabilities unify data, customer demand and insights to help you drive profitability, manage inventory and stay competitive.
Customer-centric assortment planning and optimization
- Predict customer demand by channel and forecast the impact on future sales.
- Use predictive and prescriptive analytics and advanced clustering to understand a customer's path to purchase.
- Identify opportunities to improve performance and increase profitability.
- Determine the best assortment mix based on available shelf space and financial objectives.
- Optimize merchandising decisions to improve the customer experience.
Merchandise location planning
- Use trade area analysis to determine local in-store and online merchandising, inventory and pricing decisions.
- Automate processes to predict sales by location, create relevant assortments, optimize inventory, make pricing decisions that resonate with customers, and support e-commerce fulfillment initiatives.
- Increase margin potential by understanding historical sales and true size demand.
- Forecast quantities down to store/size level.
- Recommend pack configurations to meet supply chain constraints and distribution costs.
- Gain incremental margin by optimizing profitable price strategies over product life cycles.
- Use analytics to understand competitor pricing, shape demand and meet financial goals.
How does a US regional department store develop strategic initiatives and plan financial strategies that drive profitability?
SAS helped the department store:
- Undergo a total technology transformation that includes a new merchandising platform supporting omnichannel strategies and initiatives.
- Maximize inventory precision by product and location through localized assortments.
- Drive supply chain and store operation efficiencies through optimized pack recommendations.
- Increase revenue, boost profitability and improve the customer experience.
How does the world's largest supplier of athletic shoes and apparel know the right pricing strategy for each product life cycle?
SAS helped the multinational retailer:
- Automate the decision-making process.
- Develop a product life cycle pricing structure that helped reduce markdowns.
- Understand competitor pricing and plan strategies that improve gross margin.
- Generate significant additional annual revenue by developing pricing strategies for each stage of the product life cycle.
How does the largest cosmetics and beauty retailer in Russia create customer-centric assortments to meet local demand both online and offline?
SAS helped the cosmetics and beauty retailer:
- Create an integrated business planning process from strategy to execution.
- Improve localized assortment to meet customer demand.
- Increase revenue and profit margins through improved inventory precision.
- Reduce out-of-stock and overstocked inventory imbalances.
Related Products & Solutions
- SAS® Data ManagementEnsure better, more reliable data integrated from any source.
- SAS® Enterprise Miner™Streamline the data mining process to create highly accurate predictive and descriptive models based on large volumes of data.
- SAS® Integrated Merchandise PlanningCreate merchandise plans to meet strategic plan goals and targets.
- SAS® Revenue Optimization SuiteOptimize lifecycle pricing strategies and corporate profitability with a comprehensive view of consumer demand.
- SAS® Size Optimization: SAS® Size Profiling and SAS® Pack OptimizationImprove profitability by identifying and supplying the right sizes to the right stores at the right time.
- SAS® Visual AnalyticsVisually explore all data, discover new patterns and publish reports to the web and mobile devices.
- SAS® Visual Data Mining and Machine LearningSolve your most complex problems faster with a single, integrated in-memory environment.
- SAS® Visual InvestigatorAddress a wide variety of intelligence analysis and investigation management needs with speed and precision.
- SAS® Visual StatisticsCreate and modify predictive models faster than ever using a visual interface and in-memory processing.